How do you help machine learning teams innovate faster without compromising trust? By building the platform that enables them to experiment, validate, and deploy models with confidence. As part of our Machine Learning Experimentation & Model Validation team, you'll design the tools and infrastructure that accelerate innovation while ensuring rigorous validation before production deployment.
At the intersection of backend development, cloud infrastructure, and machine learning, you'll build services and tooling that make experimentation reproducible, validation reliable, and model deployment seamless. Working alongside an experienced team, you'll help bridge the gap between experimentation and production by delivering practical solutions that drive impact across Coveo.
As one of our Senior Backend Software Developers, ML Platform, you will:- Design, build, and evolve backend services and tooling that support the entire machine learning development lifecycle.
- Develop cloud-native infrastructure and automation using Python, Amazon Web Services (AWS), Terraform, and continuous integration and continuous delivery (CI/CD) practices.
- Partner directly with machine learning engineers and data scientists to understand their workflows, identify friction points, and deliver impactful improvements.
- Integrate and maintain modern machine learning tooling, including systems such as SageMaker Studio and MLflow, while continuously improving the platform's developer experience.
- Balance technical excellence with product thinking by making thoughtful trade-offs that maximize value for internal users.
- Contribute to a collaborative engineering culture by sharing knowledge, providing technical leadership, and driving continuous improvement across the team.
- Professional experience building backend applications in Python, along with strong experience developing cloud-native solutions using Amazon Web Services (AWS).
- Experience building or maintaining machine learning platforms, MLOps tooling, or infrastructure that supports machine learning workflows.
- Strong understanding of infrastructure as code using Terraform and modern continuous integration and continuous delivery (CI/CD) practices.
- Demonstrated ability to work autonomously, communicate effectively with technical stakeholders, and influence decisions through collaboration and pragmatism.
- Experience integrating or supporting platforms such as SageMaker Studio or similar machine learning tooling.
- Familiarity with Java or experience working in polyglot engineering environments.
- Previous experience as a machine learning engineer or data scientist with a passion for building developer tooling.
- A strong product mindset with a genuine interest in improving user experience through thoughtful engineering decisions.
Do you think you can bring this role to life? Send us your application, we want to hear from you!
Join the Coveolife!
We encourage all qualified candidates to apply regardless of, for example, age, gender, disability, gaps in CV, national or ethnic background.
This job description was written by humans, assisted by AI. We may leverage technology in our hiring process to help us see the person behind the resume.
Coveo is committed to providing accessible employment practices. If you require accommodation due to a disability at any point during the recruitment process, please contact [email protected] to discuss your needs.
Skills Required
- Professional experience building backend applications in Python
- Strong experience developing cloud-native solutions using Amazon Web Services (AWS)
- Experience building or maintaining machine learning platforms, MLOps tooling, or infrastructure that supports machine learning workflows
- Strong understanding of infrastructure as code using Terraform
- Experience with modern continuous integration and continuous delivery (CI/CD) practices
- Demonstrated ability to work autonomously and communicate effectively with technical stakeholders
- Experience integrating or supporting platforms such as SageMaker Studio or similar machine learning tooling
- Familiarity with Java or experience working in polyglot engineering environments
- Previous experience as a machine learning engineer or data scientist
- Strong product mindset and interest in improving developer experience
Coveo Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Coveo and has not been reviewed or approved by Coveo.
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Healthcare Strength — Health coverage and wellness programs include 24/7 telemedicine, mental health support, and on‑site fitness amenities where available. External recognitions underscore a holistic approach to employee wellbeing.
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Wellbeing & Lifestyle Benefits — On‑site gyms, group fitness classes, healthy snacks, ergonomic/home‑office support, volunteer days, and donation matching are emphasized. Hybrid work and flexibility further support day‑to‑day wellbeing.
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Parental & Family Support — Maternity and parental leave top‑ups, the option to extend leave, and on‑site daycare in Quebec City support families. Policies are positioned to ease return‑to‑work and provide practical support for caregivers.
Coveo Insights
What We Do
Coveo powers the digital experiences of the world’s most innovative brands serving millions of people and billions of interactions across every digital experience. After a decade of enriching our market-leading platform with forward-thinking global enterprises, we know what it takes to gain a trusted AI-experience advantage. We strongly believe that the future is business-to-person, that experience is today’s competitive front line, a make or break for every business. For enterprises to achieve this AI-experience advantage at scale, it is imperative to have an Enterprise Spinal and composable ability to deliver AI semantic search and generative experiences at each customer and employee interaction.
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